Patient, Hospital and Geographic Factors Associated With Intraoperative Neuromonitoring for Cervical Spine Surgery: A National Analysis
Bibliographic record
Abstract
STUDY DESIGN: Retrospective, multi-center cohort study using a nationally representative U.S. inpatient database. OBJECTIVE: To assess national trends in intraoperative neuromonitoring (IONM) use during cervical spine surgery for degenerative cervical myelopathy (DCM) and examine patient-, procedural-, and hospital-level factors associated with its use, focusing on socioeconomic and regional variation. SUMMARY OF BACKGROUND DATA: Intraoperative neuromonitoring (IONM) is widely used to detect impending neurologic injury during cervical spine surgery, but evidence supporting its routine use remains inconclusive. Patterns of utilization may be shaped not only by clinical considerations but also by systemic, institutional, and financial factors. METHODS: We analyzed 2016-2022 National Inpatient Sample data for adults (≥18 y) undergoing cervical decompression and/or fusion for DCM, excluding trauma, infection, or neoplasm. The primary outcome was IONM use. We fit a survey-weighted multivariable logistic regression model to characterize patient, treatment and hospital-level factors associated with IONM use. A separate multilevel model with hospital-specific random intercept was used to generate a median odds ratio to characterize between-hospital variability. U.S. Census Divisions were also included to examine regional variation. RESULTS: Among 144,769 admissions for DCM surgery, IONM was used in 29% of cases, increasing from 23% in 2016 to 34% in 2022. Independent associations included private insurance, higher income, fusion procedures, posterior and anterior plus posterior approaches, and treatment at urban and private-investor hospitals (all P<0.05). IONM was more likely in the Pacific, Middle-Atlantic, West-South-Central, and Mountain divisions and less likely in the West-North-Central and East-South-Central regions. The median OR of 3.04 indicated substantial hospital-level variation. CONCLUSION: Although IONM use for DCM has increased over time, substantial heterogeneity persists. This variation is partly explained by measured clinical, sociodemographic, and hospital factors, but likely also reflects unmeasured differences in case mix. Future work integrating richer clinical and qualitative data is needed to clarify these drivers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".